Overview

Dataset statistics

Number of variables10
Number of observations400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.4 KiB
Average record size in memory80.3 B

Variable types

Text2
Categorical8

Alerts

"기타지역".1 is highly overall correlated with "기타지역" and 1 other fieldsHigh correlation
"산".1 is highly overall correlated with "특수지역" and 2 other fieldsHigh correlation
"기타지역" is highly overall correlated with "산" and 4 other fieldsHigh correlation
"산" is highly overall correlated with "특수지역" and 2 other fieldsHigh correlation
"특수지역" is highly overall correlated with "산" and 1 other fieldsHigh correlation
"기타지역".2 is highly overall correlated with "기타지역" and 1 other fieldsHigh correlation
"0" is highly overall correlated with "기타지역"High correlation
"0" is highly imbalanced (69.3%)Imbalance
"0".1 is highly imbalanced (90.3%)Imbalance
"10212 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:33:23.835011
Analysis finished2023-12-10 06:33:25.154291
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

"10212
Text

UNIQUE 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-10T15:33:25.579880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.885
Min length4

Characters and Unicode

Total characters2754
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique400 ?
Unique (%)100.0%

Sample

1st row"102120
2nd row"102121
3rd row"102122
4th row"102123
5th row"102124
ValueCountFrequency (%)
102120 1
 
0.2%
100379 1
 
0.2%
10039 1
 
0.2%
100388 1
 
0.2%
100387 1
 
0.2%
100386 1
 
0.2%
100385 1
 
0.2%
100384 1
 
0.2%
100383 1
 
0.2%
100382 1
 
0.2%
Other values (390) 390
97.5%
2023-12-10T15:33:26.364687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 806
29.3%
1 623
22.6%
" 400
14.5%
2 190
 
6.9%
6 147
 
5.3%
9 129
 
4.7%
3 127
 
4.6%
7 97
 
3.5%
8 93
 
3.4%
4 77
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2354
85.5%
Other Punctuation 400
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 806
34.2%
1 623
26.5%
2 190
 
8.1%
6 147
 
6.2%
9 129
 
5.5%
3 127
 
5.4%
7 97
 
4.1%
8 93
 
4.0%
4 77
 
3.3%
5 65
 
2.8%
Other Punctuation
ValueCountFrequency (%)
" 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 806
29.3%
1 623
22.6%
" 400
14.5%
2 190
 
6.9%
6 147
 
5.3%
9 129
 
4.7%
3 127
 
4.6%
7 97
 
3.5%
8 93
 
3.4%
4 77
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 806
29.3%
1 623
22.6%
" 400
14.5%
2 190
 
6.9%
6 147
 
5.3%
9 129
 
4.7%
3 127
 
4.6%
7 97
 
3.5%
8 93
 
3.4%
4 77
 
2.8%

"48720330"
Text

Distinct74
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-10T15:33:26.702684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)10.5%

Sample

1st row"41113670"
2nd row"28237582"
3rd row"28237582"
4th row"28237582"
5th row"11470560"
ValueCountFrequency (%)
28200581 45
 
11.2%
28237570 44
 
11.0%
28200650 34
 
8.5%
28237530 28
 
7.0%
28237591 20
 
5.0%
28237582 17
 
4.2%
28237520 17
 
4.2%
28237581 15
 
3.8%
28200710 14
 
3.5%
28200580 14
 
3.5%
Other values (64) 152
38.0%
2023-12-10T15:33:27.274970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 800
20.0%
2 707
17.7%
0 607
15.2%
8 448
11.2%
5 332
8.3%
1 304
 
7.6%
3 289
 
7.2%
7 277
 
6.9%
6 109
 
2.7%
4 82
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3200
80.0%
Other Punctuation 800
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 707
22.1%
0 607
19.0%
8 448
14.0%
5 332
10.4%
1 304
9.5%
3 289
9.0%
7 277
 
8.7%
6 109
 
3.4%
4 82
 
2.6%
9 45
 
1.4%
Other Punctuation
ValueCountFrequency (%)
" 800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
" 800
20.0%
2 707
17.7%
0 607
15.2%
8 448
11.2%
5 332
8.3%
1 304
 
7.6%
3 289
 
7.2%
7 277
 
6.9%
6 109
 
2.7%
4 82
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 800
20.0%
2 707
17.7%
0 607
15.2%
8 448
11.2%
5 332
8.3%
1 304
 
7.6%
3 289
 
7.2%
7 277
 
6.9%
6 109
 
2.7%
4 82
 
2.1%

"특수지역"
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"주거지역"
232 
"상업지역"
75 
"특수지역"
48 
"기타지역"
 
20
"공업지역"
 
18

Length

Max length7
Median length6
Mean length6.0175
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"주거지역"
2nd row"주거지역"
3rd row"주거지역"
4th row"주거지역"
5th row"상업지역"

Common Values

ValueCountFrequency (%)
"주거지역" 232
58.0%
"상업지역" 75
 
18.8%
"특수지역" 48
 
12.0%
"기타지역" 20
 
5.0%
"공업지역" 18
 
4.5%
"오피스지역" 7
 
1.8%

Length

2023-12-10T15:33:27.578677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:33:27.762619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 232
58.0%
상업지역 75
 
18.8%
특수지역 48
 
12.0%
기타지역 20
 
5.0%
공업지역 18
 
4.5%
오피스지역 7
 
1.8%

"산"
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"고밀주거지역"
128 
"중밀주거지역"
47 
"주택상업지"
41 
"저밀주거지역"
34 
"일반상업지"
31 
Other values (11)
119 

Length

Max length8
Median length8
Mean length7.0875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"저밀주거지역"
2nd row"고밀주거지역"
3rd row"고밀주거지역"
4th row"고밀주거지역"
5th row"복합상업지"

Common Values

ValueCountFrequency (%)
"고밀주거지역" 128
32.0%
"중밀주거지역" 47
 
11.8%
"주택상업지" 41
 
10.2%
"저밀주거지역" 34
 
8.5%
"일반상업지" 31
 
7.8%
"혼합지역" 23
 
5.8%
"기타지역" 20
 
5.0%
"산" 20
 
5.0%
"공업중심지역" 16
 
4.0%
"공원" 16
 
4.0%
Other values (6) 24
 
6.0%

Length

2023-12-10T15:33:27.982667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고밀주거지역 128
32.0%
중밀주거지역 47
 
11.8%
주택상업지 41
 
10.2%
저밀주거지역 34
 
8.5%
일반상업지 31
 
7.8%
혼합지역 23
 
5.8%
기타지역 20
 
5.0%
산 20
 
5.0%
공업중심지역 16
 
4.0%
공원 16
 
4.0%
Other values (6) 24
 
6.0%

"산".1
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"고밀주거지역"
128 
"중밀주거지역"
47 
"저밀주거지역"
34 
"일반상업지"
31 
"고밀주거상업"
31 
Other values (16)
129 

Length

Max length8
Median length8
Mean length7.1525
Min length3

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row"저밀주거지역"
2nd row"고밀주거지역"
3rd row"고밀주거지역"
4th row"고밀주거지역"
5th row"복합상업지"

Common Values

ValueCountFrequency (%)
"고밀주거지역" 128
32.0%
"중밀주거지역" 47
 
11.8%
"저밀주거지역" 34
 
8.5%
"일반상업지" 31
 
7.8%
"고밀주거상업" 31
 
7.8%
"혼합지역" 23
 
5.8%
"산" 20
 
5.0%
"기타지역" 17
 
4.2%
"공원" 16
 
4.0%
"공업중심지역" 14
 
3.5%
Other values (11) 39
 
9.8%

Length

2023-12-10T15:33:28.195090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고밀주거지역 128
32.0%
중밀주거지역 47
 
11.8%
저밀주거지역 34
 
8.5%
일반상업지 31
 
7.8%
고밀주거상업 31
 
7.8%
혼합지역 23
 
5.8%
산 20
 
5.0%
기타지역 17
 
4.2%
공원 16
 
4.0%
공업중심지역 14
 
3.5%
Other values (11) 39
 
9.8%

"기타지역"
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"주거지역"
269 
"준주거지역"
73 
"기타지역"
33 
"공업지역"
 
21
"상업지역"
 
3

Length

Max length7
Median length6
Mean length6.185
Min length6

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row"준주거지역"
2nd row"주거지역"
3rd row"주거지역"
4th row"주거지역"
5th row"주거지역"

Common Values

ValueCountFrequency (%)
"주거지역" 269
67.2%
"준주거지역" 73
 
18.2%
"기타지역" 33
 
8.2%
"공업지역" 21
 
5.2%
"상업지역" 3
 
0.8%
"오피스지역" 1
 
0.2%

Length

2023-12-10T15:33:28.404644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:33:28.595391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 269
67.2%
준주거지역 73
 
18.2%
기타지역 33
 
8.2%
공업지역 21
 
5.2%
상업지역 3
 
0.8%
오피스지역 1
 
0.2%

"기타지역".1
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"중밀주거지역"
121 
"저밀주거지역"
82 
"주택상업지"
73 
"고밀주거지역"
66 
"기타지역"
33 
Other values (5)
25 

Length

Max length8
Median length8
Mean length7.6275
Min length6

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row"주택상업지"
2nd row"중밀주거지역"
3rd row"고밀주거지역"
4th row"중밀주거지역"
5th row"저밀주거지역"

Common Values

ValueCountFrequency (%)
"중밀주거지역" 121
30.2%
"저밀주거지역" 82
20.5%
"주택상업지" 73
18.2%
"고밀주거지역" 66
16.5%
"기타지역" 33
 
8.2%
"공업중심지역" 14
 
3.5%
"주거공업지" 7
 
1.8%
"복합상업지" 2
 
0.5%
"주택오피스가" 1
 
0.2%
"일반상업지" 1
 
0.2%

Length

2023-12-10T15:33:28.839604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:33:29.076412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중밀주거지역 121
30.2%
저밀주거지역 82
20.5%
주택상업지 73
18.2%
고밀주거지역 66
16.5%
기타지역 33
 
8.2%
공업중심지역 14
 
3.5%
주거공업지 7
 
1.8%
복합상업지 2
 
0.5%
주택오피스가 1
 
0.2%
일반상업지 1
 
0.2%

"기타지역".2
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"중밀주거지역"
121 
"저밀주거지역"
82 
"주거상업"
73 
"고밀주거지역"
66 
"기타지역"
33 
Other values (5)
25 

Length

Max length8
Median length8
Mean length7.4675
Min length6

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row"주거상업"
2nd row"중밀주거지역"
3rd row"고밀주거지역"
4th row"중밀주거지역"
5th row"저밀주거지역"

Common Values

ValueCountFrequency (%)
"중밀주거지역" 121
30.2%
"저밀주거지역" 82
20.5%
"주거상업" 73
18.2%
"고밀주거지역" 66
16.5%
"기타지역" 33
 
8.2%
"공업중심지역" 14
 
3.5%
"주거공업지역" 7
 
1.8%
"혼합상업지역" 2
 
0.5%
"주택오피스가" 1
 
0.2%
"일반상업지" 1
 
0.2%

Length

2023-12-10T15:33:29.339137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:33:29.556140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중밀주거지역 121
30.2%
저밀주거지역 82
20.5%
주거상업 73
18.2%
고밀주거지역 66
16.5%
기타지역 33
 
8.2%
공업중심지역 14
 
3.5%
주거공업지역 7
 
1.8%
혼합상업지역 2
 
0.5%
주택오피스가 1
 
0.2%
일반상업지 1
 
0.2%

"0"
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"0"
378 
"1"
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"0"
2nd row"0"
3rd row"0"
4th row"0"
5th row"0"

Common Values

ValueCountFrequency (%)
"0" 378
94.5%
"1" 22
 
5.5%

Length

2023-12-10T15:33:29.772434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:33:29.926792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 378
94.5%
1 22
 
5.5%

"0".1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"0"
395 
"1"
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"0"
2nd row"0"
3rd row"0"
4th row"0"
5th row"0"

Common Values

ValueCountFrequency (%)
"0" 395
98.8%
"1" 5
 
1.2%

Length

2023-12-10T15:33:30.086293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:33:30.251674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 395
98.8%
1 5
 
1.2%

Correlations

2023-12-10T15:33:30.360701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"48720330""특수지역""산""산".1"기타지역""기타지역".1"기타지역".2"0""0".1
"48720330"1.0000.8040.8300.8690.9430.9100.9100.8670.751
"특수지역"0.8041.0001.0001.0000.8460.7300.7300.3080.000
"산"0.8301.0001.0001.0000.7920.7570.7570.2910.207
"산".10.8691.0001.0001.0000.8150.7740.7740.2450.132
"기타지역"0.9430.8460.7920.8151.0001.0001.0000.6900.000
"기타지역".10.9100.7300.7570.7741.0001.0001.0000.6450.000
"기타지역".20.9100.7300.7570.7741.0001.0001.0000.6450.000
"0"0.8670.3080.2910.2450.6900.6450.6451.0000.000
"0".10.7510.0000.2070.1320.0000.0000.0000.0001.000
2023-12-10T15:33:30.558334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"기타지역".1"산".1"기타지역""0""산""0".1"특수지역""기타지역".2
"기타지역".11.0000.4120.9950.4950.4170.0000.4951.000
"산".10.4121.0000.5260.2100.9930.1130.9810.412
"기타지역"0.9950.5261.0000.5040.5290.0000.4650.995
"0"0.4950.2100.5041.0000.2250.0000.2210.495
"산"0.4170.9930.5290.2251.0000.1590.9870.417
"0".10.0000.1130.0000.0000.1591.0000.0000.000
"특수지역"0.4950.9810.4650.2210.9870.0001.0000.495
"기타지역".21.0000.4120.9950.4950.4170.0000.4951.000
2023-12-10T15:33:30.738684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"특수지역""산""산".1"기타지역""기타지역".1"기타지역".2"0""0".1
"특수지역"1.0000.9870.9810.4650.4950.4950.2210.000
"산"0.9871.0000.9930.5290.4170.4170.2250.159
"산".10.9810.9931.0000.5260.4120.4120.2100.113
"기타지역"0.4650.5290.5261.0000.9950.9950.5040.000
"기타지역".10.4950.4170.4120.9951.0001.0000.4950.000
"기타지역".20.4950.4170.4120.9951.0001.0000.4950.000
"0"0.2210.2250.2100.5040.4950.4951.0000.000
"0".10.0000.1590.1130.0000.0000.0000.0001.000

Missing values

2023-12-10T15:33:24.840922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:33:25.060745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

"10212"48720330""특수지역""산""산".1"기타지역""기타지역".1"기타지역".2"0""0".1
0"102120"41113670""주거지역""저밀주거지역""저밀주거지역""준주거지역""주택상업지""주거상업""0""0"
1"102121"28237582""주거지역""고밀주거지역""고밀주거지역""주거지역""중밀주거지역""중밀주거지역""0""0"
2"102122"28237582""주거지역""고밀주거지역""고밀주거지역""주거지역""고밀주거지역""고밀주거지역""0""0"
3"102123"28237582""주거지역""고밀주거지역""고밀주거지역""주거지역""중밀주거지역""중밀주거지역""0""0"
4"102124"11470560""상업지역""복합상업지""복합상업지""주거지역""저밀주거지역""저밀주거지역""0""0"
5"102126"28237582""상업지역""일반상업지""일반상업지""주거지역""중밀주거지역""중밀주거지역""0""0"
6"102127"28237582""주거지역""중밀주거지역""중밀주거지역""주거지역""중밀주거지역""중밀주거지역""0""0"
7"102128"28237581""상업지역""주택상업지""고밀주거상업""주거지역""중밀주거지역""중밀주거지역""0""0"
8"102129"28237582""주거지역""고밀주거지역""고밀주거지역""주거지역""중밀주거지역""중밀주거지역""0""0"
9"10213"11230600""기타지역""기타지역""기타지역""주거지역""중밀주거지역""중밀주거지역""0""0"
"10212"48720330""특수지역""산""산".1"기타지역""기타지역".1"기타지역".2"0""0".1
390"101004"28200710""공업지역""공업중심지역""공업중심지역""공업지역""공업중심지역""공업중심지역""0""0"
391"101005"28200710""공업지역""공업중심지역""공업중심지역""공업지역""공업중심지역""공업중심지역""0""0"
392"101007"28200700""주거지역""고밀주거지역""고밀주거지역""준주거지역""주택상업지""주거상업""0""0"
393"101008"28200710""공업지역""공업중심지역""공업업무지역""공업지역""공업중심지역""공업중심지역""0""0"
394"101009"28200710""공업지역""공업중심지역""공업중심지역""공업지역""공업중심지역""공업중심지역""0""0"
395"101010"28200690""주거지역""중밀주거지역""중밀주거지역""준주거지역""주택상업지""주거상업""0""0"
396"101011"28200710""공업지역""공업중심지역""공업중심지역""공업지역""주거공업지""주거공업지역""0""0"
397"101012"28200710""공업지역""공업중심지역""공업중심지역""공업지역""공업중심지역""공업중심지역""0""0"
398"101013"28200700""주거지역""고밀주거지역""고밀주거지역""준주거지역""주택상업지""주거상업""0""0"
399"101014"28200710""공업지역""공업중심지역""공업중심지역""공업지역""공업중심지역""공업중심지역""0""0"